This is the Article Published by Luiss Guido Carli University (one of the main Business Schools in Italy) as honour and award regarding the exceptional achievement: https://www.luiss.it/news/2020/03/20/stefano-ciccarelli-combinare-programmazione-e-finanza
Luiss Guido Carli & Warwick Business School – QTEM Accademic Partners
Since the Quantitative Easing is showing its inefficacy to sustain the real economic growth, and the potential increase of the “Sharing Repurchase” Policy – given a possible win of Democrats in the US that will bring to an increase of the Corporate Tax rate – the perceived risk of an expected increase of the corporations Financial Leverage would bring to a greater demanded return from the Institutional Investors and lenders, that can affect and reduce the financial institutions’ margins.
To survive, the environment incentives can return to the ones of the 2008 – financial actors can more likely find new ways to cheat on the real risk of the underlying to mitigate the possibility of a marginal reduction of effective profits.
This can poison the market, and reduce the level of trust. Also given the Coronavirus effects on the Short & Midterms regarding local economies, the situation can become really hard to analyze.
In these conditions, for portfolio managers and investors building a balanced asset allocation strategy can be very difficult: is necessary to be dynamic and fast to react to a constantly changing information environment.
When together with my Team (QTEaM – Composed by 4 QTEM Students) we applied to the Warwick Business School (a Worldwide leading University for Finance) Investment Challenge, I was conscious of the challenges would expect us to build a competitive portfolio – also given the high level of competition of the other participants.
Consequently, we adopted an innovative approach to maximize the market adaptability with respect to immediate macroeconomics non-linear risk factors. A strategy possible thanks to combining the classical financial theory with coding abilities that can allow a consistent just-in-time alpha identification.
From the strategical perspective, the methodology approach used had various degrees of analysis, starting with a bottom-up approach:
- Identify the Macro-Economics Principal Drivers (QE, QT, Brexit, US-China Trade Deals, etc.)
- Estimate the most affected Industries, given these trends, with a time horizon of at least one year.
- In each Industry – using also the Sharpe Ratio – Identify the top-performing companies, that can maximize the flexibility and the reaction to hypothetical market trends inversions.
This phase required web-scraping and data-mining techniques to work on a large scale. - Collect the list of the Tickers and insert them in a portfolio optimization algorithm to obtain the percentage to allocate for each stock to maximize the return obtained for each level of volatility inserted in our portfolio.
More technical details here. - Combining the stocks together with ETF, Bonds, and Commodities (like US soybean, given the fact that after the China’s Coronavirus the local production can be affected and the rigid demand can push the Chinese government to lower the tariffs – to prevent the Brazilian Soybean’s Price to increase ).
After a competitive phase, we classified together with the other 2 teams for the finals.
Following the presentation of the results, there was a “surprise challenge“: basically a new macroeconomic scenario was provided and we had to change our portfolio composition given the new expected trends in a few minutes.
The proactiveness is the most important skill for a portfolio manager, and the same is for its portfolio.
Once determined the effects, it was possible to shift in a small amount of time (given the algorithm fast execution) the composition that would maximize our new volatility-adjusted expected return.
The financial experts evaluated our results and our performance also with the following Q&A to understand deeply the insights and the techniques we used in our approach: Finally, we arrived first and following there was a networking session with them.
This strategy and those skills (Python, AWS, etc.) are effective and used more and more in real life by asset managers to pursue successfully their returns. Consequently, for the future of this job is it strongly suggested to learn Data Science and Coding skills to adapt to the changing skills demand – the best investment each individual can do.
